首页|Studies from Pangasinan State University in the Area of Machine Learning Publish ed (Sentiment Analysis of Students' Feedback on Faculty Online Teaching Performa nce Using Machine Learning Techniques)
Studies from Pangasinan State University in the Area of Machine Learning Publish ed (Sentiment Analysis of Students' Feedback on Faculty Online Teaching Performa nce Using Machine Learning Techniques)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on artificial in telligence have been published. According to news reporting out of Pangasinan St ate University by NewsRx editors, research stated, "The pandemic has given rise to challenges across different sectors, particularly in educational institutions . The mode of instruction has shifted from in-person to flexible learning, leadi ng to increased stress and concerns for key stakeholders such as teachers, paren ts, and students. The ongoing spread of diseases has made in-person classes unfe asible." Our news journalists obtained a quote from the research from Pangasinan State Un iversity: "Even if limited face to face classes will be allowed, online teaching is deemed to remain a practice to support instructional delivery to students. T herefore, it is essential to understand the challenges and issues encountered in online teaching, particularly from the perspective of students. This knowledge is crucial for supervisors and administrators, as it provides insights to aid in planning intervention measures. These interventions can support teachers in enh ancing their online teaching performance for the benefit of their students. A pr ocess that can be applied to achieve this goal is sentiment analysis. In the fie ld of education, one of the applications of sentiment analysis is in the evaluat ion of faculty teaching performance. It has been a practice in educational insti tutions to periodically assess their teachers' performance. However, it has not been easy to take into account the students' comments due to the lack of methods for automated text analytics. In line with this, techniques in sentiment analys is are presented in this study. Base models such as Naive Bayes, Support Vector Machines, Logistic Regression, and Random Forest were explored in experiments an d compared to a combination of the four called ensemble. Outcomes indicate that the ensemble of the four outperformed the base models. The utilization of Ngram vectorization in conjunction with ensemble techniques resulted in the highest F1 score compared to Count and TF-IDF methods. Additionally, this approach achieve d the highest Cohen's Kappa and Matthews Correlation Coefficient (MCC), along wi th the lowest Cross-entropy, signifying its preference as the model of choice fo r sentiment classification."
Pangasinan State UniversityCyborgsEm erging TechnologiesMachine Learning
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